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this is a base project to simply show creating the supplementary data files and show casing how this simple concept can be used to create maintainable that is easily extendable and has the tools required to manage complexity.
to understand this document browse it as if you would any website; exploring is important and baked into it
anyone can write code a computer can understand; the real challenge is writing in a way that humans can
when a field becomes sufficiently advanced it reinvents algebra; so lets skip straight to algebra
we write software/code to manage complexities; without these tools it would be impossible to do much of all we do; but at the end of the day it is just a tool and picking the right tool for the job is important. when the only tool you have is a hammer everything looks like a nail below I will outline some tools that will help us manage the complexities of working in PGDP; especially remotely.
this document you are reading is a rmarkdown; it is magic as far as I am concerned it combines some powerful yet simple ideas but it ultimately allows you to communicate your purpose; documentation , logic , and code, work report, management tool, training document, research paper, data product, visualization tool, presentation material, panflet and hand out generator, report tool and many other uses in one manageable place; seriously just watch this. it is truly a marvel. more on literate programming here for more info but the jist is this
Literate programming is a programming paradigm introduced by Donald Knuth in which a computer program is given an explanation of its logic in a natural language, such as English, interspersed with snippets of macros and traditional source code, from which compilable source code can be generated.[1] The approach is used in scientific computing and in data science routinely for reproducible research and open access purposes.[2] Literate programming tools are used by millions of programmers today
rmarkdown gives you headings that collapse to further structure this, allowing you to jump between section very quickly
this here is a piece of code using a package called ggplot2; the data is from the world bank and is called gapminder it is a simple data set on life expectancy and gdp per country.
ggplot uses a grammar for describing visual components
library(ggplot2)
data(gapminder, package = "gapminder")
gg <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
geom_point(aes(size = pop, frame = year, ids = country)) +
scale_x_log10()Warning: Ignoring unknown aesthetics: frame, idsplotly::ggplotly(gg)library(ggplot2)
library(tidyverse)-- Attaching packages --------------------------------------- tidyverse 1.3.0 --v tibble 3.0.3 v dplyr 1.0.2
v tidyr 1.1.2 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.0
v purrr 0.3.4 -- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()pgdp <- cansim::get_cansim( "36-10-0402-02") %>% select(REF_DATE,GEO,Value,VALUE,`North American Industry Classification System (NAICS)`) %>% pivot_wider(names_from = Value, values_from = VALUE) Accessing CANSIM NDM product 36-10-0402 from Statistics CanadaParsing dataFolding in metadata#
gg <- ggplot(pgdp, aes(`Contributions to percent change`, `Current dollars`, color = GEO)) +
geom_point(aes(size = `Chained (2012) dollars`, frame = REF_DATE, ids = `North American Industry Classification System (NAICS)`)) +
scale_x_log10()Warning: Ignoring unknown aesthetics: frame, idsplotly::ggplotly(gg)Warning in self$trans$transform(x): NaNs producedWarning: Transformation introduced infinite values in continuous x-axis